A Case Study on the Design and Operational Impact of a Modular Digital Twin for Drilling Rigs with GenAI Component Life Models

Abstract This paper presents a scalable digital twin solution designed to improve drilling equipment reliability amid increasingly demanding operational conditions. Built on three core pillars: data integration, advanced analytics, and human-centered design; the system combines real-time equipment monitoring with predictive models to quantify asset health and proactively address failure risks. A robust data pipeline processes over 200 GB of data daily, supporting Artificial Intelligence (AI)- and physics-based models that forecast the health of more than 7,200 components under varying geologic and operating conditions. Deployed across more than 100 rigs in 18 months, the solution reduced equipment-related downtime by more than 75%, failure-related credits by 95%, and normalized operating costs by 38%. Rigs using the system demonstrated a 13% year-over-year improvement in drilling performance and a 28% gain versus peers. Developed on a low-code, functional block programming platform, the system achieved commercial deployment within three months. This approach accelerated development, enabled rapid user feedback integration, and demonstrated a practical pathway for large-scale GenAI-enabled reliability solutions in drilling operations.